Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT.

Michael Dieckmeyer,Claus Zimmer,Malek El Husseini,Karupppasamy Subburaj,Maximilian T Löffler,Jan S Kirschke,Nico Sollmann,Anjany Sekuboyina,Thomas Baum

doi:10.3389/fendo.2021.792760

Abstract

PurposeTo identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework.MethodsWe performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30–90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δrel<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Varianceglobal measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction.ResultsSix of the 24 extracted TFs had Δrel<5% (Varianceglobal, Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Varianceglobal,SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Varianceglobal and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001–0.002).ConclusionLong-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.

Highlights

Texture analysis (TA) is an emerging subfield of radiomics, representing a non-invasive and objective method for the quantitative assessment of medical images
With TFFU and TFBL denoting the measurements of a certain Texture feature (TF) at FU and BL, respectively, and averaged across all vertebrae (T4 - L5) for each patient
Of the 24 analyzed TFs, four features showed a relative difference between FU and BL measurements (Drel) below 1%, and two features between 1% and 5% (Table 2)

Summary

Introduction

Texture analysis (TA) is an emerging subfield of radiomics, representing a non-invasive and objective method for the quantitative assessment of medical images. Texture features (TFs) can be used to quantitatively characterize image properties, such as uniformity, heterogeneity and randomness, as well as repetitive image patterns. Applications of TA include neuroimaging, musculoskeletal and oncological imaging, e.g. to assess tumor heterogeneity [2, 3]. TA has been performed across different anatomical regions and modalities, with CT being the modality used most frequently, arguably due to its multitude of clinical applications, broad availability and data quality [4]. Musculoskeletal applications have been based on radiographic, magnetic resonance imaging (MRI) [5, 6] and CT data [7,8,9]

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